Abstract
We present a new architecture of time delay-reservoir reservoir computer based on the polarization dynamics of a VCSEL. This architecture achieves state of the art performance. Moreover, implementing polarization rotated feedback instead of a polarization preserving feedback allows further improvement to the computing performance.
This enhancement of the reservoir computing performance is demonstrated on a telecommunication task: nonlinear channel equalization. Numerical simulations show better performance while using the polarization rotated feedback compared to isotropic feedback. This first insight is also proved experimentally and experiment and theory agree not only qualitatively, but also quantitatively.
Supported by Ministère de l’Enseignement Supérieur de la Recherche et de l’Innovation; Région Grand-Est; Département Moselle; European Regional Development Fund (ERDF); Metz Métropole; Airbus GDI Simulation; CentraleSupélec; Fondation CentraleSupélec.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Appeltant, L., et al.: Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011). https://doi.org/10.1038/ncomms1476
Duport, F., Schneider, B., Smerieri, A., Haelterman, M., Massar, S.: All-optical reservoir computing. Opt. Express 20(20), 22783–22795 (2012). https://doi.org/10.1364/OE.20.022783
Larger, L., et al.: Photonic information processing beyond turing: an optoelectronic implementation of reservoir computing. Opt. Express 20(3), 3241–3249 (2012). https://doi.org/10.1364/OE.20.003241
Nguimdo, R.M., Verschaffelt, G., Danckaert, J., der Sande, G.V.: Fast photonic information processing using semiconductor lasers with delayed optical feedback: role of phase dynamics. Opt. Express 22(7), 8672–8686 (2014). https://doi.org/10.1364/OE.22.008672
Vatin, J., Rontani, D., Sciamanna, M.: Enhanced performance of a reservoir computer using polarization dynamics in VCSELs. Opt. Lett. 43(18), 4497–4500 (2018). https://doi.org/10.1364/OL.43.004497
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Vatin, J., Rontani, D., Sciamanna, M. (2019). Polarization Dynamics of VCSELs Improves Reservoir Computing Performance. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-30493-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30492-8
Online ISBN: 978-3-030-30493-5
eBook Packages: Computer ScienceComputer Science (R0)